With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current data architectures are not necessarily designed to keep up with the scale and scope of data and analytics use cases. In fact, existing architectures often fail to deliver the promised value associated with them. Data mesh is a socio-technical, decentralized, distributed concept for enterprise data management. As the concept of data mesh is still novel, it lacks empirical insights from the field. Specifically, an understanding of the motivational factors for introducing data mesh, the associated challenges, implementation strategies, its business impact, and potential archetypes is missing. To address this gap, we conduct 15 semi-structured interviews with industry experts. Our results show, among other insights, that organizations have difficulties with the transition toward federated governance associated with the data mesh concept, the shift of responsibility for the development, provision, and maintenance of data products, and the comprehension of the overall concept. In our work, we derive multiple implementation strategies and suggest organizations introduce a cross-domain steering unit, observe the data product usage, create quick wins in the early phases, and favor small dedicated teams that prioritize data products. While we acknowledge that organizations need to apply implementation strategies according to their individual needs, we also deduct two archetypes that provide suggestions in more detail. Our findings synthesize insights from industry experts and provide researchers and professionals with preliminary guidelines for the successful adoption of data mesh.

This study investigates Serious Games (SG) to assess motivational factors appropriate to an Intelligent Tutoring System (ITS). An ITS can benefit from SG’ elements that can highly support learners’ motivation. Thus, identifying and assessing the effect that these factors may have on learners is a crucial step before attempting to integrate them into an ITS. We designed an experiment using a Serious Game and combined both the theoretical ARCS model of motivation and empirical physiological sensors (heart rate, skin conductance and EEG) to assess the effects of motivational factors on learners. We then identified physiological patterns correlated with one motivational factor in a Serious Game (Alarm triggers) associated with the Attention category of the ARCS model. The best result of three classifiers run on the physiological data has reached an accuracy of 73.8% in identifying learners’ attention level as being either above or below average. These results open the door to the possibility for an ITS to discriminate between attentive and inattentive learners.